The importance of interpretability and visualization in machine learning for applications in medicine and health care
Open accessAbstract
In a short period of time, many areas of science have made a sharp transition towards data-dependent methods. In some cases, this process has been enabled by simultaneous advances in data acquisition and the development of networked system technologies. This new situation is particularly clear in the life sciences, where data overabundance has sparked a flurry of new methodologies for data management and analysis. This can be seen as a perfect scenario for the use of machine learning and computational intelligence techniques to address problems in which more traditional data analysis approaches might struggle. But, this scenario also poses some serious challenges. One of them is model interpretability and explainability, especially for complex nonlinear models. In some areas such as medicine and health care, not addressing such challenge might seriously limit the chances of adoption, in real practice, of computer-based systems that rely on machine learning and computational intelligence methods for data analysis. In this paper, we reflect on recent investigations about the interpretability and explainability of machine learning methods and discuss their impact on medicine and health care. We pay specific attention to one of the ways in which interpretability and explainability in this context can be addressed, which is through data and model visualization. We argue that, beyond improving model interpretability as a goal in itself, we need to integrate the medical experts in the design of data analysis interpretation strategies. Otherwise, machine learning is unlikely to become a part of routine clinical and health care practice.
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Different applications usually have different interpretability and explainability needs, which works against generalizable solutions that span a number of situations.
“Unfortunately, secondary factors also complicate solutions, for instance, different applications usually have different interpretability and explainability needs [20] working against generalizable solutions that span a number of situations.”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [20]
ML-based systems are unlikely to be adopted in routine medical practice beyond a limited number of niche applications unless challenges are addressed, most likely by having systems provide satisfactory explanations for their decisions.
“It has been argued that despite the existence of much evidence supporting their usefulness, ML-based systems are unlikely to be adopted in routine medical practice beyond a limited number of niche applications unless these challenges are addressed, most likely by having systems provide satisfactory explanations for their decisions [20,21].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [20]
Currently, most of the decisions made by AI-based CDSS cannot be interpreted in a transparent way, potentially limiting the uptake, trust, and usability of these systems in practice.
“Currently, most of the decisions made by AI-based CDSS cannot be interpreted in a transparent way potentially limiting the uptake, trust, and usability of these systems in practice [20,133].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [20]
Models should be built in collaboration with input from those with expertise from the fields of social and behavioral science, philosophy, psychology, and cognitive science.
“Models should be built in collaboration with input from those with expertise from the fields of social and behavioral science, philosophy, psychology, and cognitive science [19,20,64].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [20]
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- Authors
- Vellido, A.
- Affiliations
- Universitat Politècnica de Catalunya (ES)
- Corresponding author
- Vellido, Alfredo
- ORCID
- Vellido, Alfredo
- Venue
- Neural Computing and Applications
- Published
- 2019-02-04
- Volume
- 32
- Issue
- 24
- Pages
- 18069-18083
- ISSN
- 0941-0643 (ISSN-L)
- 1433-3058
- OpenAlex
- W2913240367
- Semantic Scholar
- 9abd1ff6214eacd65a8ba7f790e9e87c86088cb9
- Cited by
- 635
- References
- 70
- Citations by year
- 2026: 65 · 2025: 117 · 2024: 133 · 2023: 106 · 2022: 105 · 2021: 81 · 2020: 22 · 2019: 6
- FWCI
- 24.41
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Neural Computing and Applications (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (acceptedVersion, other-oa)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (acceptedVersion, other-oa)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (submittedVersion, public-domain)
- Funders
- MINECO (TIN2016-79576-R)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- model interpretability, explainability, data visualization, machine learning in medicine, health care, nonlinear models, computational intelligence
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